1. Field
The present invention relates to systems for determining product attribute levels. In some aspects, the invention relates to systems used for determining respondent preferences based on a set of product attribute levels specific to the respondent.
2. Discussion
Product manufacturers are presented with many choices during the design of a product. For each of several product attributes, a manufacturer must choose from among several attribute levels to include in a product. These choices may be facilitated by obtaining information relating to consumer preferences.
A consumer may be any entity to which a product and/or service may be offered, including individuals or businesses. Preference information can be used to determine the popularity and desirability of particular product attributes and attribute levels to such consumers. Therefore, preference information may allow a manufacturer to choose product configurations as well as production amounts and prices for each product configuration that maximize overall profit.
Significant resources are currently expended to obtain detailed and accurate consumer preference information. These resources are most commonly allotted to conventional consumer surveys, which typically consist of a list of predetermined questions designed to elicit information from a consumer regarding the consumer's feelings toward products, product attributes, and product attribute levels. Surveys may be administered randomly, for example by stopping consumers at shopping malls or other retail areas, or by contacting specific consumers who are targeted because they are members of a demographic group about which information is desired.
Conventional surveys present several inherent drawbacks. First, since survey results are compiled into general demographic categories, surveys merely determine, at best, preferences of a theoretical average consumer belonging to each demographic category. Accordingly, survey results are only marginally correlated to any one consumer's preferences. Therefore, such results lack predictive precision of a particular consumer's preferences with respect to marketplace choices available and not yet available. Second, although conventional surveys may indicate whether one attribute level (e.g. “sedan body style”) is generally preferred over another level of the same attribute (“coupe body style color”), such surveys do not provide any reliable means for comparing preferences across attributes. For example, conventional surveys are generally unable to determine the degree to which a consumer prefers a sedan body style over another body style so as to enable comparison between that degree and the degree to which the consumer prefers a 150 horsepower engine over an engine generating a different amount of horsepower. As a result of these drawbacks, conventional surveys do not adequately produce useful preference information.
Focus groups are another conventional vehicle used to obtain consumer preference information. In a typical focus group, certain consumers are randomly selected (or selected based on demographics as described above) to answer questions and/or to participate in a group discussion regarding a product or a type of product. Answers and comments of the consumers are noted and tabulated to create preference information similar to that obtained using survey techniques. Due to their interactive nature, focus groups may elicit more useful information than that elicited by surveys. Despite this advantage, focus groups still suffer from the drawbacks described above with respect to conventional surveys.
Trade-off analysis techniques attempt to address the above and other deficiencies in conventional techniques for determining consumer preference information. Generally, trade-off analysis techniques attempt to quantify a consumer's preference for particular product attributes and attribute levels. Such quantification is intended to allow a manufacturer to easily and accurately compare the attractiveness of various product configurations to a consumer. These comparisons are possible because the trade-off techniques associate a particular numerical value with a consumer's preference for each attribute and attribute level. Accordingly, the relative attractiveness of any attribute or attribute level with respect to any other attribute or attribute level can be determined simply by comparing the appropriate associated numerical values.
According to one classification scheme, four types of trade-off analysis techniques exist: conjoint; discrete choice; self-explicated; and hybrid. Conjoint analysis generally requires a consumer to rate or rank product configurations with respect to one another. Typically, the consumer is asked to rank twenty to thirty product configurations. Each ranked configuration includes different combinations of attributes and attribute levels being evaluated. By appropriately varying the configurations, a regression model can be estimated for each consumer.
Conjoint analysis is an improvement over conventional systems for determining consumer preferences. For example, determining preferences by observing consumer behavior is difficult because consumer behavior can usually be observed only with respect to a few combinations of attributes and attribute levels (i.e., the combinations that exist in the marketplace). Accordingly, it becomes difficult to separate and distinguish between the preferences of different consumers and to predict effects of changes in attributes and/or attribute levels on consumer behavior. On the other hand, conjoint analysis allows for improved learning of consumer preferences through controlled variation and controlled co-variation of attributes and attribute levels.
According to discrete choice analysis, a consumer is presented with a set of product configurations and asked to select either the configuration that the consumer is most interested in purchasing or no configuration if the consumer is not interested in purchasing any of the presented configurations. The process is then repeated for other sets of product configurations. In contrast to conjoint analysis, which may be used to estimate a regression model for individual consumers, discrete choice analysis may be used to estimate a mixture method (similar to a regression model) for a group of consumers.
While conjoint analysis and discrete choice analysis determine consumers' preferences indirectly, self-explicated analysis directly determines preferences by asking consumers how important each product attribute range and attribute level range is to their purchasing decisions. According to some self-explicated analysis models, consumers are presented with all attributes and attribute levels to be evaluated, and asked to identify attribute levels that are unacceptable. An unacceptable attribute level is one that, if included in a product, would cause the product to be completely unacceptable to the consumer, regardless of any other attributes and attribute levels included in the product. For example, a consumer may indicate that an automobile including an attribute level of “pink” associated with the attribute “color” is completely unacceptable regardless of any other attributes or attribute levels included in the automobile. Accordingly, “pink” is identified as an unacceptable attribute level for that consumer.
Next, the consumer is asked to identify, from the acceptable attribute levels, the most-desirable and the least-desirable attribute levels associated with each presented attribute. Assuming that the consumer's most important attribute has a rating of 100, the consumer is then asked to rank the relative importance of each remaining attribute from 0 to 100. Next, for each attribute, the desirability of each attribute level is rated with respect to all other acceptable attribute levels of the attribute. A consumer preference for an attribute level is then obtained by multiplying the relative importance of its associated attribute by its desirability rating.
Hybrid analysis techniques utilize a combination of features from the above-described techniques. The most common example of a hybrid analysis technique is Adaptive Conjoint Analysis (ACA), a product of Sawtooth Software, Inc. According to ACA, a consumer is taken through several rankings of attribute levels and ratings of relative attribute importance (similar to self-explicated techniques) and then asked to identify, for each of a series of pairs of product configurations, which one of the pair is the most desirable and the degree to which it is more desirable. Other examples of hybrid models include the Cake Method and the Logit-Cake Method developed by MACRO Consulting, Inc.
Each of these trade-off analysis techniques requires consumers to provide consistent, thoughtful responses to presented inquiries. A consumer may be able to provide such responses if presented with a small number of inquiries, but is unlikely to do so if presented with many inquiries. In this regard, the number of inquiries presented by each of the above techniques increases sharply as the number of evaluated attributes and/or attribute levels increases. Such an increase in the number of inquiries also causes a corresponding increase in the amount of time required to answer the inquiries. Therefore, as more attributes and attribute levels are evaluated, various forms of consumer bias are likely to increase, such as a waning attention span, a lack of time, a lack of patience, boredom, and haste. These increased consumer biases result in increased consumer error and inaccurate preference information. Also increased is a consumer's tendency to abandon the technique and to simply cease answering further inquiries, in which case the resulting preference information is partially or totally unusable.
Another form of consumer bias is caused by consumer attitudes toward particular attributes and/or attribute levels. As described above, conventional trade-off analysis techniques ask a consumer to evaluate the importance of an attribute or attribute level with respect to other attributes or attribute levels. However, if the consumer has an extreme dislike for one of the attributes or attribute levels, the consumer may overestimate the importance of the other attributes or attribute levels.
Each of the foregoing shortcomings might be addressed by reducing a number of attribute levels that are considered during trade-off analysis. However, reducing the number of attribute levels may cause an unsatisfactory decrease in the accuracy and comprehensiveness of preference information generated by existing systems. Moreover, existing systems do not provide any efficient process for determining those attribute levels that may generate satisfactory preference information for a particular consumer.